基于图像扩散速度模型和纹理信息的人脸活体检测

计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 112-117.doi: 10.11896/jsjkx.181202339

• 计算机图形学&多媒体 • 上一篇下一篇

基于图像扩散速度模型和纹理信息的人脸活体检测

李新豆,高陈强,周风顺,韩慧,汤林

(重庆邮电大学通信与信息工程学院信号与信息处理重庆市重点实验室重庆400065)

收稿日期:2018-12-17 出版日期:2020-02-15 发布日期:2020-03-18
通讯作者: 高陈强(gaocq@cqupt.edu.cn)
基金资助:
国家自然科学基金(61571071);重庆市科委自然科学基金(cstc2014jcyjA40048)

Face Liveness Detection Based on Image Diffusion Speed Model and Texture Information

LI Xin-dou,GAO Chen-qiang,ZHOU Feng-shun,HAN Hui,TANG Lin

(Chongqing Key Laboratory of Signal and Information Processing,College of Information and Communication Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

Received:2018-12-17 Online:2020-02-15 Published:2020-03-18
About author:LI Xin-dou,born in 1992,postgraduate.His main research interests indude face liveness detection;GAO Chen-qiang,born in 1981,Ph.D,professor.His research interests include image processing,infrared target detection and event detection.
Supported by:
This work was supported by the National Natural Science Foundation of China (61571071) and Natural Science Foundation of Chongqing Science and Technology Commission (cstc2014jcyjA40048).

摘要/Abstract

摘要： 为了解决人脸身份认证中的欺诈问题,提出了一种基于图像扩散速度模型和纹理信息的人脸活体检测算法。真实人脸和虚假人脸图像的空间结构不同,为了提取这种差异特征,该方法使用各向异性扩散增强图像的边缘信息。然后,将原始图像与扩散后图像的差值作为图像的扩散速度,并构建扩散速度模型。接着使用局部二值算法提取图像扩散速度特征并训练分类器。真实人脸图像和虚假人脸图像之间存在很多差异特征,为了进一步提高人脸活体检测算法的泛化能力,该方法同时提取人脸图像的模糊程度特征和色彩纹理特征,通过特征矩阵级联的方法将两种特征进行融合,并训练另一个分类器。最后根据分类器输出概率加权融合的结果做出判决。实验结果表明,该算法能够快速有效地检测出虚假的人脸图像。

关键词: 各向异性扩散, 活体检测, 局部区域二值, 人脸识别

Abstract: To solve the problem of fraud in face authentication,this paper proposed a face liveness detection algorithm based on image diffusion speed model and texture information.The spatial structures of real face and fake face images are different.In order to extract difference features,anisotropic diffusion is used to enhance image edge information.And then,the difference between the original image and the diffused image is used as the image diffusion speed,and a diffusion velocity model is contructed.Then,local binary pattern algorithm is used to extract the diffusion speed feature and train a classifier.There are many differences between real face images and fake face images.In order to further improve the generalization ability of face liveness detection,the blur degree and color feature of face image are extracted synchronously.These features are combined by cascading feature matrix and another classifier is trained.Finally,a judgment is made based on the probabilities weighted fusion result of classifier output.Experimental results show that the proposed algorithm can detect spoofing faces quickly and efficiently.

Key words: Anisotropic diffusion, Face liveness detection, Face recognition, Local binary pattern

中图分类号:

TP391.41

李新豆,高陈强,周风顺,韩慧,汤林. 基于图像扩散速度模型和纹理信息的人脸活体检测[J]. 计算机科学, 2020, 47(2): 112-117. https://doi.org/10.11896/jsjkx.181202339

LI Xin-dou,GAO Chen-qiang,ZHOU Feng-shun,HAN Hui,TANG Lin. Face Liveness Detection Based on Image Diffusion Speed Model and Texture Information[J]. Computer Science, 2020, 47(2): 112-117. https://doi.org/10.11896/jsjkx.181202339

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